Daniel R. Jiang

According to our database, Daniel R. Jiang authored at least 18 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Weakly Coupled Deep Q-Networks.

Ibrahim El Shar, Daniel R. Jiang

CoRR, 2023

Faster Approximate Dynamic Programming by Freezing Slow States.

Yijia Wang, Daniel R. Jiang

CoRR, 2023

2022

Dynamic Inventory Repositioning in On-Demand Rental Networks.

Saif Benjaafar, Daniel R. Jiang, Xiang Li, Xiaobo Li

Manag. Sci., 2022

Interpretable Personalized Experimentation.

Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies.

Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy

CoRR, 2021

Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs.

Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds.

Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell

Oper. Res., 2020

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization.

Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees.

Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Lookahead-Bounded Q-learning.

Ibrahim El Shar, Daniel R. Jiang

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Exploration via Sample-Efficient Subgoal Design.

Yijia Wang, Matthias Poloczek, Daniel R. Jiang

CoRR, 2019

BoTorch: Programmable Bayesian Optimization in PyTorch.

Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy

CoRR, 2019

2018

Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures.

Daniel R. Jiang, Warren B. Powell

Math. Oper. Res., 2018

Feedback-Based Tree Search for Reinforcement Learning.

Daniel R. Jiang, Emmanuel Ekwedike, Han Liu

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds.

Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell

CoRR, 2017

2015

An Approximate Dynamic Programming Algorithm for Monotone Value Functions.

Daniel R. Jiang, Warren B. Powell

Oper. Res., 2015

Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming.

Daniel R. Jiang, Warren B. Powell

INFORMS J. Comput., 2015

2014

A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work?

Daniel R. Jiang, Thuy V. Pham, Warren B. Powell, Daniel F. Salas, Warren R. Scott

Proceedings of the 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2014